Wald tests for the fixed effects. The random effects are reported as
standard deviations and correlations (varcorr) with Wald intervals
built on a transformed scale and mapped back: \(\exp\) of the interval
for \(\log SD\), \(\tanh\) of the interval for
\(\mathrm{atanh}(\rho)\) (delta method from the packed Cholesky
parameters). No test or p-value is given for them: a z-test of
\(\log SD\) tests \(SD = 1\), and \(SD = 0\) lies on the boundary;
use anova.brsmm (chi-bar-square mixture) against the model
without the term. The randomized quantile residuals are drawn without
changing the caller's RNG state.
Usage
# S3 method for class 'brsmm'
summary(object, level = 0.95, ...)Value
Object of class "summary.brsmm"; coefficients$random
holds the packed Cholesky parameters (estimate and standard error only)
and varcorr the SD/correlation table.
Examples
# \donttest{
dat <- data.frame(
y = c(
0, 5, 20, 50, 75, 90, 100, 30, 60, 45,
10, 40, 55, 70, 85, 25, 35, 65, 80, 15
),
x1 = rep(c(1, 2), 10),
id = factor(rep(1:4, each = 5))
)
prep <- brs_prep(dat, ncuts = 100)
#> brs_prep: n = 20 | exact = 0, left = 1, right = 1, interval = 18
fit <- brsmm(y ~ x1, random = ~ 1 | id, data = prep)
s <- summary(fit)
s$coefficients$mean
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) 0.4213003 0.880329 0.4785714 0.6322436
#> x1 -0.3374483 0.536152 -0.6293892 0.5290943
# }